The process of determining the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire inter-node distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1 % (33cm). The localization algorithm is based on Least Squares Scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multi-dimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor

Advances in processor, memory sad radio technology will enable small axed cheap nodes capable of sensing, communi- cation ad computation. Networks of such nodes can coor- dinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is data- centtic in that all communication is for named data. All nodes in a directed diffusion-based network are applicationaware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore ad evaluate the use of directed diffusion for a simple remote-surveillance sensor network.